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CONTRIBUTING.md
using a job called "copybara". **5. Copy to Google Internal codebase and run internal CI** - Once the PR is in the Google codebase, we make sure it integrates well with its dependencies and the rest of the system. - Rarely, If the tests fail at this stage, we cannot merge the code. - If needed, we may come to you to make some changes. At times, it may not be
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ci/official/utilities/setup_macos.sh
chmod +x "/usr/local/bin/bazel" fi # "TFCI_MACOS_UPGRADE_PYENV_ENABLE" is used to decide if we need to upgrade the # Pyenv version. We enable this for macOS x86 builds as the default Pyenv on # those VMs does not support installing Python 3.12 and above which we need # for running smoke tests in nightly/release wheel builds. if [[ "${TFCI_MACOS_UPGRADE_PYENV_ENABLE}" == 1 ]]; then
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SECURITY.md
whether these uses were recommended or considered safe, or where we recommend some form of isolation when dealing with untrusted data. As a result, this document also outlines what issues we consider as TensorFlow security vulnerabilities. We recognize issues as vulnerabilities only when they occur in scenarios that we outline as safe; issues that have a security impact only when TensorFlow is used
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ci/official/requirements_updater/requirements.in
typing_extensions == 4.8.0 gast == 0.4.0 termcolor == 2.3.0 wrapt == 1.16.0 tblib == 2.0.0 # Install tensorboard, and keras # Note that here we want the latest version that matches TF major.minor version # Note that we must use nightly here as these are used in nightly jobs # For release jobs, we will pin these on the release branch keras-nightly ~= 3.0.0.dev tb-nightly ~= 2.17.0.a # Test dependencies grpcio >= 1.24.3, < 2.0
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tensorflow/c/experimental/gradients/array_grad_test.cc
}; TEST_P(CppGradients, TestIdentityNGrad) { // This test is interesting because the current implementation of GradientTape // would return [0, 1] whereas we use build_default_zeros_grads=false here // so we get back [nullptr, 1]. AbstractTensorHandlePtr x1; { AbstractTensorHandle* x1_raw = nullptr; status_ = TestScalarTensorHandle<float, TF_FLOAT>(
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ci/official/containers/linux_arm64/builder.devtoolset/build_devtoolset.sh
"${TARGET}/usr/lib64" # Link in architecture specific includes from the system; note that we cannot # link in the whole aarch64-linux-gnu folder, as otherwise we're overlaying # system gcc paths that we do not want to find. # TODO(klimek): Automate linking in all non-gcc / non-kernel include # directories. mkdir -p "${TARGET}/usr/include/aarch64-linux-gnu"
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tensorflow/api_template.__init__.py
# limitations under the License. # ============================================================================== """ Top-level module of TensorFlow. By convention, we refer to this module as `tf` instead of `tensorflow`, following the common practice of importing TensorFlow via the command `import tensorflow as tf`. The primary function of this module is to import all of the public TensorFlow
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.github/workflows/release-branch-cherrypick.yml
# will be created. name: Release Branch Cherrypick on: workflow_dispatch: inputs: # We use this instead of the "run on branch" argument because GitHub looks # on that branch for a workflow.yml file, and we'd have to cherry-pick # this file into those branches. release_branch: description: 'Release branch name (e.g. r2.9)' required: true
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ci/official/containers/linux_arm64/cuda.packages.txt
libcudnn8=8.9.6.50-1+cuda12.2 libcudnn8-dev=8.9.6.50-1+cuda12.2 # This can be removed once NVIDIA publishes a cuda-12.3.2 Docker image. # For now it ensures that we install at least version 12.3.107 of PTXAS, # since 12.3.103 has a bug.
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tensorflow/c/eager/c_api_unified_experimental.h
// Core APIs // ----------------------------------------------------------------------------- // A TF_ExecutionContext stores knowledge about how to execute an operation. // E.g. it could know whether we're in eager mode or graph mode, keeps track // of gradient tapes, etc. typedef struct TF_ExecutionContext TF_ExecutionContext; // A TF_AbstractTensor is an input to an operation. E.g. it could be a union
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